similar to: more structure than 'str'?

What are the functions available for use with “aggregate”? Where can a reference to them be found? --------------------------------- [[alternative HTML version deleted]]

Dear R users, I have a question on using weighted.mean() while aggregating a data frame. I have a data frame with columns Sub, Length and Slope: > x[1:5,] Sub Length Slope 1 2 351.547 0.0025284969 2 2 343.738 0.0025859390 3 1 696.659 0.0015948968 4 2 5442.338 0.0026132544 5 1 209.483 0.0005304225 and I would like to calculate the weighted.mean of Slope, using Length

How can I extract K (number of parameters) from an AIC calculation, both to report K itself and to calculate AICc? I'm aware of the conversion from AIC -> AICc, where AICc = AIC + 2K(K+1)/(n-K-1), but not sure of how K is calculated or how to extract that value from either an AIC or logLik calculation. This is probably more of a basic statistics question than an R question, but I thank

Hi All I have been fitting regression models and would now like to produce some contour & image plots from the predictors. Is there an easy way to do this? My current (newbie) experience with R would suggest there is but that it's not always easy to find it! f3 <- lm( fc ~ poly( speed, 2 ) + poly( torque, 2 ) + poly( sonl, 2 ) + poly( p_rail, 2 ) + poly( pil_sep, 2 ) + poly( maf, 2

Hola! I am programming a class (S3) "symarray" for storing the results of functions symmetric in its k arguments. Intended use is for association indices for more than two variables, for instance coresistivity against antibiotics. There is one programming problem I haven't solved, making an inverse of the index function indx() --- se code below. It could for instance return the

Hi, To obtain estimates for some missing values in my data I fitted a linear regression and then used the command fitted(model) to get the fitted values from the model, but R doesn't return any values for the NA's. I can calculate the fitted values from the estimates obtained from the summary of model, but that's not very handy. Is there a way to include the missing values in the

Hi, Does anyone know how to compute the confidence prediction intervals for a nonlinear least squares models (nls)? I was trying to use the function 'predict' as I usually do for other models fitting (glm, lm, gams...), but it seems that se.fit, and interval computation is not implemented for the nls... Cheers Enrique ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Fisheries Research Services, Marine

I am encountering an error with plot.lm: > tstdf <- data.frame( y=c(1.01,1.98,3.02,3.99),x=c(1,2,3,4)) > plot(lm(I(y) ~ x, data=tstdf)) Hit <Return> to see next plot: Hit <Return> to see next plot: Error in object$coefficients : $ operator is invalid for atomic vectors Obviously I don't need the I() in this example, but I have been working with regressions that involve

Hi, The question is as follows: has anyone coded panel data unit root tests with R? Even the "first generation" tests (see e.g. Levin & Lin 1993; Pesaran, & Smith & Im 1996; Maddala & Wu 1999) would be sufficient for my needs. To my understanding, these are rather easy to code, but as I have taken just my first steps in coding with R, existing code would save me

Hello and sorry to disturb. I'm trying to plot the confidence intervals for the fixed effects of a lme. I want to obtain graphically, if it is possible, a bar with Estimate, upper and lower CI for each level of the factors. I know how to do for a lm model but for a lme one, I tried with plot(intervals(...)) and plot(ci(...)) from the gmodels package but it doesn't work well. Thanks

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

Hi, I have a complex-valued vector X in C^n. Given another complex-valued vector Y in C^n, I want to find a permutation of Y, say, Y*, that minimizes ||X - Y*||, the distance between X and Y*. Note that this problem can be trivially solved for "Real" vectors, since real numbers possess the ordering property. Complex numbers, however, do not possess this property. Hence the

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

I am looking for hints on how to estimate ratings for competitors in an ongoing pairwise competition using R... my particular area of interest being the game of Go, but the idea of identifying ratings (on a continuous scale) rather than relative rankings seems easily generalized to other competitions so I thought someone might be studying something related already. I presume the rating of a

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

Hello Coming from matlab background, I could use some help on this one. I need to generate a data set based on this equation X(t) = 3.8x(t-1) (1-x(t-1)) + e(t), where e(t) is a N(0,0,001) random variable I need say 100 values. How do I do this? Thanks

Hello, has someone written by chance a function to extract the variance-covariance matrix from a lmer-object? I've noticed the VarCorr function, but it gives unhandy output. Regards, Roel de Jong

Hello, All: If I use LazyData with the Ecdat package on R-Forge, "R CMD check" reports "no visible binding for global variable 'nonEnglishNames'", where 'nonEnglishNames' is a dataset in Ecdat used as the default argument for a function. With LazyData, that NOTE disappears. However, then I get, "Warning: objects 'Hstarts',

Dear all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

Dear all, I am trying to define my own corStruct which is different from the classical one available in nlme. The structure of this correlation is given below. I am wondering to know how to continue with this structure by using specific functions (corMatrix, getCovariate, Initialize,...) in order to get a structure like corAR1, corSymm which will be working for my data. Thanks in advance.